






"""simple docstring"""


import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						return 1.0 / (1.0 + np.exp(-_outputs ))


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Union[str, Any]						=			np.max(_outputs     ,      axis=-1     ,      keepdims=a__ )
						lowerCAmelCase   :Tuple						=			np.exp(_outputs - maxes )
						return shifted_exp / shifted_exp.sum(axis=-1     ,      keepdims=a__ )




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				Optional[int]      =  """sigmoid"""
					lowercase_    :				Dict      =  """softmax"""
					lowercase_    :				List[str]      =  """none"""




@add_end_docstrings(
    UpperCamelCase   ,     r"""
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `\"default\"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `\"sigmoid\"`: Applies the sigmoid function on the output.
            - `\"softmax\"`: Applies the softmax function on the output.
            - `\"none\"`: Does not apply any function on the output.
    """   ,     )
class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				Any      =  False
					lowercase_    :				int      =  ClassificationFunction.NONE
					def __init__(							self							:	Dict     , **UpperCAmelCase							:	Dict   )	->       Dict:
											super().__init__(**UpperCAmelCase   )

											self.check_model_type(
											    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
											    if self.framework == 'tf'
											    else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING   )
					def        UpperCAmelCase__ (							self							:	Tuple     , UpperCAmelCase							:	Optional[int]=None     , UpperCAmelCase							:	Tuple=None     , UpperCAmelCase							:	Optional[int]=""     , **UpperCAmelCase							:	Dict   )	->       Tuple:
											# Using "" as default argument because we're going to use `top_k=None` in user code to declare
											# "No top_k"
											lowerCAmelCase   :Optional[Any]						=			tokenizer_kwargs

											lowerCAmelCase   :Any						=			{}
											if hasattr(self.model.config     , 'return_all_scores'   ) and return_all_scores is None:
																	lowerCAmelCase   :Tuple						=			self.model.config.return_all_scores

											if isinstance(UpperCAmelCase     , UpperCAmelCase   ) or top_k is None:
																	lowerCAmelCase   :Dict						=			top_k
																	lowerCAmelCase   :List[Any]						=			False
											elif return_all_scores is not None:
																	warnings.warn(
																	    '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
																	    ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'     , UpperCAmelCase     , )
																	if return_all_scores:
																							lowerCAmelCase   :int						=			None
																	else:
																							lowerCAmelCase   :Optional[Any]						=			1

											if isinstance(UpperCAmelCase     , UpperCAmelCase   ):
																	lowerCAmelCase   :int						=			ClassificationFunction[function_to_apply.upper()]

											if function_to_apply is not None:
																	lowerCAmelCase   :Dict						=			function_to_apply
											return preprocess_params, {}, postprocess_params
					def __call__(							self							:	Tuple     , *UpperCAmelCase							:	Optional[int]     , **UpperCAmelCase							:	Dict   )	->       List[Any]:
											lowerCAmelCase   :str						=			super().__call__(*UpperCAmelCase     , **UpperCAmelCase   )
											# TODO try and retrieve it in a nicer way from _sanitize_parameters.
											lowerCAmelCase   :Any						=			'top_k' not in kwargs
											if isinstance(args[0]     , UpperCAmelCase   ) and _legacy:
																	# This pipeline is odd, and return a list when single item is run
																	return [result]
											else:
																	return result
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	Union[str, Any]     , **UpperCAmelCase							:	Dict   )	->       Dict[str, GenericTensor]:
											lowerCAmelCase   :int						=			self.framework
											if isinstance(UpperCAmelCase     , UpperCAmelCase   ):
																	return self.tokenizer(**UpperCAmelCase     , return_tensors=UpperCAmelCase     , **UpperCAmelCase   )
											elif isinstance(UpperCAmelCase     , UpperCAmelCase   ) and len(UpperCAmelCase   ) == 1 and isinstance(inputs[0]     , UpperCAmelCase   ) and len(inputs[0]   ) == 2:
																	# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
																	return self.tokenizer(
																	    text=inputs[0][0]     , text_pair=inputs[0][1]     , return_tensors=UpperCAmelCase     , **UpperCAmelCase   )
											elif isinstance(UpperCAmelCase     , UpperCAmelCase   ):
																	# This is likely an invalid usage of the pipeline attempting to pass text pairs.
																	raise ValueError(
																	    'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
																	    ' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'   )
											return self.tokenizer(UpperCAmelCase     , return_tensors=UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Optional[int]     , UpperCAmelCase							:	List[str]   )	->       Any:
											return self.model(**UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	List[Any]     , UpperCAmelCase							:	str     , UpperCAmelCase							:	List[str]=None     , UpperCAmelCase							:	Any=1     , UpperCAmelCase							:	List[Any]=True   )	->       str:
											# `_legacy` is used to determine if we're running the naked pipeline and in backward
											# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
											# the more natural result containing the list.
											# Default value before `set_parameters`
											if function_to_apply is None:
																	if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
																							lowerCAmelCase   :Dict						=			ClassificationFunction.SIGMOID
																	elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
																							lowerCAmelCase   :Tuple						=			ClassificationFunction.SOFTMAX
																	elif hasattr(self.model.config     , 'function_to_apply'   ) and function_to_apply is None:
																							lowerCAmelCase   :Any						=			self.model.config.function_to_apply
																	else:
																							lowerCAmelCase   :Union[str, Any]						=			ClassificationFunction.NONE

											lowerCAmelCase   :int						=			model_outputs['logits'][0]
											lowerCAmelCase   :Union[str, Any]						=			outputs.numpy()

											if function_to_apply == ClassificationFunction.SIGMOID:
																	lowerCAmelCase   :Optional[int]						=			sigmoid(UpperCAmelCase   )
											elif function_to_apply == ClassificationFunction.SOFTMAX:
																	lowerCAmelCase   :Union[str, Any]						=			softmax(UpperCAmelCase   )
											elif function_to_apply == ClassificationFunction.NONE:
																	lowerCAmelCase   :List[Any]						=			outputs
											else:
																	raise ValueError(f"""Unrecognized `function_to_apply` argument: {function_to_apply}"""   )

											if top_k == 1 and _legacy:
																	return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

											lowerCAmelCase   :Tuple						=			[
											    {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(UpperCAmelCase   )
											]
											if not _legacy:
																	dict_scores.sort(key=lambda UpperCAmelCase   : x["score"]     , reverse=UpperCAmelCase   )
																	if top_k is not None:
																							lowerCAmelCase   :Tuple						=			dict_scores[:top_k]
											return dict_scores







"""simple docstring"""


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			len(a__ )
						print('The following activities are selected:' )

						# The first activity is always selected
						lowerCAmelCase   :Dict						=			0
						print(a__     ,      end=',' )

						# Consider rest of the activities
						for j in range(a__ ):
												# If this activity has start time greater than
												# or equal to the finish time of previously
												# selected activity, then select it
												if start[j] >= finish[i]:
																		print(a__     ,      end=',' )
																		lowerCAmelCase   :List[str]						=			j


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		__SCREAMING_SNAKE_CASE   =   [1, 3, 0, 5, 8, 5]
		__SCREAMING_SNAKE_CASE   =   [2, 4, 6, 7, 9, 9]
		print_max_activities(start, finish)